





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
         TimesformerForVideoClassification,
         TimesformerModel,
     )
     from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from transformers import VideoMAEImageProcessor



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self							:  Optional[Any]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  Union[str, Any]=13			,					UpperCAmelCase_							:  List[Any]=10			,					UpperCAmelCase_							:  Optional[int]=3			,					UpperCAmelCase_							:  Optional[Any]=2			,					UpperCAmelCase_							:  int=2			,					UpperCAmelCase_							:  Optional[Any]=True			,					UpperCAmelCase_							:  str=True			,					UpperCAmelCase_							:  int=32			,					UpperCAmelCase_							:  Tuple=5			,					UpperCAmelCase_							:  Union[str, Any]=4			,					UpperCAmelCase_							:  Tuple=37			,					UpperCAmelCase_							:  Tuple="gelu"			,					UpperCAmelCase_							:  Union[str, Any]=0.1			,					UpperCAmelCase_							:  List[str]=0.1			,					UpperCAmelCase_							:  List[Any]=10			,					UpperCAmelCase_							:  Optional[int]=0.02			,					UpperCAmelCase_							:  Union[str, Any]="divided_space_time"			,					UpperCAmelCase_							:  Dict=None			,					)			->Any:



   '''simple docstring'''



   lowerCamelCase__: str         =parent
   lowerCamelCase__: Optional[int]         =batch_size
   lowerCamelCase__: List[Any]         =image_size
   lowerCamelCase__: Union[str, Any]         =num_channels
   lowerCamelCase__: List[str]         =patch_size
   lowerCamelCase__: Dict         =num_frames
   lowerCamelCase__: List[str]         =is_training
   lowerCamelCase__: List[Any]         =use_labels
   lowerCamelCase__: Tuple         =hidden_size
   lowerCamelCase__: List[str]         =num_hidden_layers
   lowerCamelCase__: Union[str, Any]         =num_attention_heads
   lowerCamelCase__: List[Any]         =intermediate_size
   lowerCamelCase__: Union[str, Any]         =hidden_act
   lowerCamelCase__: Optional[int]         =hidden_dropout_prob
   lowerCamelCase__: Optional[Any]         =attention_probs_dropout_prob
   lowerCamelCase__: List[Any]         =attention_type
   lowerCamelCase__: Union[str, Any]         =initializer_range
   lowerCamelCase__: str         =scope
   lowerCamelCase__: List[str]         =num_labels

   # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
   lowerCamelCase__: Optional[int]         =(image_size // patch_size) ** 2
   lowerCamelCase__: Any         =(num_frames) * self.num_patches_per_frame + 1

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Dict         =floats_tensor(
       [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

   lowerCamelCase__: Any         =None
   if self.use_labels:
     lowerCamelCase__: Dict         =ids_tensor([self.batch_size]			,					self.num_labels)

   lowerCamelCase__: Dict         =self.get_config()

   return config, pixel_values, labels

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Any:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =TimesformerConfig(
       image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					num_frames=self.num_frames			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					initializer_range=self.initializer_range			,					attention_type=self.attention_type			,					)
   lowerCamelCase__: Tuple         =self.num_labels
   return config

 def 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Optional[Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =TimesformerModel(config=_A)
   model.to(_A)
   model.eval()
   lowerCamelCase__: int         =model(_A)
   self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  int)			->str:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =TimesformerForVideoClassification(_A)
   model.to(_A)
   model.eval()

   lowerCamelCase__: Tuple         =model(_A)

   # verify the logits shape
   lowerCamelCase__: Tuple         =torch.Size((self.batch_size, self.num_labels))
   self.parent.assertEqual(result.logits.shape			,					_A)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->str:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.prepare_config_and_inputs()
   lowerCamelCase__: List[Any]         =config_and_inputs
   lowerCamelCase__: Any         ={'''pixel_values''': pixel_values}
   return config, inputs_dict



@require_torch
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 lowercase_						=     (
     {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 lowercase_						=     False
 lowercase_						=     False
 lowercase_						=     False
 lowercase_						=     False

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->str:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =TimesformerModelTester(self)
   lowerCamelCase__: Any         =ConfigTester(
       self			,					config_class=_A			,					has_text_modality=_A			,					hidden_size=37)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  str=False)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: int         =copy.deepcopy(_A)

   if return_labels:
     if model_class in get_values(_A):
       lowerCamelCase__: Any         =torch.zeros(
           self.model_tester.batch_size			,					dtype=torch.long			,					device=_A)

   return inputs_dict

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Union[str, Any]:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="TimeSformer does not use inputs_embeds")
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->Optional[int]:



   '''simple docstring'''



   pass

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__: List[str]         =model_class(_A)
     self.assertIsInstance(model.get_input_embeddings()			,					(nn.Module))
     lowerCamelCase__: List[str]         =model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(_A			,					nn.Linear))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__: int         =model_class(_A)
     lowerCamelCase__: int         =inspect.signature(model.forward)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowerCamelCase__: Union[str, Any]         =[*signature.parameters.keys()]

     lowerCamelCase__: Dict         =['''pixel_values''']
     self.assertListEqual(arg_names[:1]			,					_A)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->List[str]:



   '''simple docstring'''



   lowerCamelCase__: List[str]         =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_A)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->Dict:



   '''simple docstring'''



   lowerCamelCase__: str         =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_video_classification(*_A)

 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->List[str]:



   '''simple docstring'''



   for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__: Any         =TimesformerModel.from_pretrained(_A)
     self.assertIsNotNone(_A)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Union[str, Any]:



   '''simple docstring'''



   if not self.has_attentions:
     pass

   else:
     lowerCamelCase__: Union[str, Any]         =self.model_tester.prepare_config_and_inputs_for_common()
     lowerCamelCase__: int         =True

     for model_class in self.all_model_classes:
       lowerCamelCase__: List[str]         =self.model_tester.seq_length
       lowerCamelCase__: Tuple         =self.model_tester.num_frames

       lowerCamelCase__: Union[str, Any]         =True
       lowerCamelCase__: str         =False
       lowerCamelCase__: Optional[int]         =True
       lowerCamelCase__: Any         =model_class(_A)
       model.to(_A)
       model.eval()
       with torch.no_grad():
         lowerCamelCase__: Tuple         =model(**self._prepare_for_class(_A			,					_A))
       lowerCamelCase__: Optional[Any]         =outputs.attentions
       self.assertEqual(len(_A)			,					self.model_tester.num_hidden_layers)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       lowerCamelCase__: Optional[Any]         =True
       lowerCamelCase__: Optional[int]         =model_class(_A)
       model.to(_A)
       model.eval()
       with torch.no_grad():
         lowerCamelCase__: Optional[int]         =model(**self._prepare_for_class(_A			,					_A))
       lowerCamelCase__: Any         =outputs.attentions
       self.assertEqual(len(_A)			,					self.model_tester.num_hidden_layers)

       # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
       self.assertListEqual(
           list(attentions[0].shape[-3:])			,					[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,					)
       lowerCamelCase__: str         =len(_A)

       # Check attention is always last and order is fine
       lowerCamelCase__: Optional[Any]         =True
       lowerCamelCase__: str         =True
       lowerCamelCase__: List[str]         =model_class(_A)
       model.to(_A)
       model.eval()
       with torch.no_grad():
         lowerCamelCase__: Any         =model(**self._prepare_for_class(_A			,					_A))

       self.assertEqual(out_len + 1			,					len(_A))

       lowerCamelCase__: int         =outputs.attentions

       self.assertEqual(len(_A)			,					self.model_tester.num_hidden_layers)

       # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
       self.assertListEqual(
           list(self_attentions[0].shape[-3:])			,					[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,					)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Union[str, Any]:



   '''simple docstring'''



   def check_hidden_states_output(UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  int):
     lowerCamelCase__: Tuple         =model_class(_A)
     model.to(_A)
     model.eval()

     with torch.no_grad():
       lowerCamelCase__: Optional[int]         =model(**self._prepare_for_class(_A			,					_A))

     lowerCamelCase__: Any         =outputs.hidden_states
     lowerCamelCase__: List[str]         =self.model_tester.num_hidden_layers + 1
     self.assertEqual(len(_A)			,					_A)

     lowerCamelCase__: Tuple         =self.model_tester.seq_length

     self.assertListEqual(
         list(hidden_states[0].shape[-2:])			,					[seq_length, self.model_tester.hidden_size]			,					)

   lowerCamelCase__: List[str]         =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__: Tuple         =True
     check_hidden_states_output(_A			,					_A			,					_A)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowerCamelCase__: int         =True

     check_hidden_states_output(_A			,					_A			,					_A)





def 	lowerCAmelCase_					(			)	->						List[str]:







  """simple docstring"""





  lowerCamelCase__: Optional[int]         =hf_hub_download(
      repo_id="hf-internal-testing/spaghetti-video"      ,       filename="eating_spaghetti.npy"      ,       repo_type="dataset"						)
  lowerCamelCase__: str         =np.load(__a						)
  return list(__a						)






@require_torch
@require_vision
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 @cached_property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Tuple:



   '''simple docstring'''



   return (
       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,					image_std=[0.5, 0.5, 0.5])
       if is_vision_available()
       else None
   )







 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->str:



   '''simple docstring'''



   lowerCamelCase__: List[str]         =TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400").to(
       _A)

   lowerCamelCase__: List[str]         =self.default_image_processor
   lowerCamelCase__: Union[str, Any]         =prepare_video()
   lowerCamelCase__: Any         =image_processor(video[:8]			,					return_tensors="pt").to(_A)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__: Tuple         =model(**_A)

   # verify the logits
   lowerCamelCase__: Union[str, Any]         =torch.Size((1, 400))
   self.assertEqual(outputs.logits.shape			,					_A)

   lowerCamelCase__: str         =torch.tensor([-0.3016, -0.7713, -0.4205]).to(_A)

   self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					_A			,					atol=1E-4))









'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


